HanDiffuser is a text-to-image generation model that generates realistic hands by incorporating hand embeddings into the diffusion process. The model consists of two components: a Text-to-Hand-Params diffusion model that generates SMPL-Body and MANO-Hand parameters from text prompts, and a Text-Guided Hand-Params-to-Image diffusion model that synthesizes images based on the generated hand parameters and text. HanDiffuser incorporates multiple aspects of hand representation, including 3D shapes, joint-level finger positions, orientations, and articulations, to achieve robust learning and reliable performance. The model was evaluated using quantitative and qualitative experiments, as well as user studies, demonstrating its effectiveness in generating images with high-quality hands. HanDiffuser outperforms existing methods in generating realistic hands, particularly in terms of hand pose, shape, and finger articulation. The model is trained on a curated dataset of text and image pairs, and the Text-to-Hand-Params component is trained on SMPL-H parameters, while the Text-Guided Hand-Params-to-Image component is trained on text and image pairs. The model was tested on various datasets and showed superior performance in generating realistic hands compared to other methods. The results indicate that HanDiffuser is effective in generating images with high-quality hands, and the model's ability to condition image generation on hand parameters contributes to its success. The model's architecture and training process are detailed in the paper, along with the results of user studies that validate its effectiveness.HanDiffuser is a text-to-image generation model that generates realistic hands by incorporating hand embeddings into the diffusion process. The model consists of two components: a Text-to-Hand-Params diffusion model that generates SMPL-Body and MANO-Hand parameters from text prompts, and a Text-Guided Hand-Params-to-Image diffusion model that synthesizes images based on the generated hand parameters and text. HanDiffuser incorporates multiple aspects of hand representation, including 3D shapes, joint-level finger positions, orientations, and articulations, to achieve robust learning and reliable performance. The model was evaluated using quantitative and qualitative experiments, as well as user studies, demonstrating its effectiveness in generating images with high-quality hands. HanDiffuser outperforms existing methods in generating realistic hands, particularly in terms of hand pose, shape, and finger articulation. The model is trained on a curated dataset of text and image pairs, and the Text-to-Hand-Params component is trained on SMPL-H parameters, while the Text-Guided Hand-Params-to-Image component is trained on text and image pairs. The model was tested on various datasets and showed superior performance in generating realistic hands compared to other methods. The results indicate that HanDiffuser is effective in generating images with high-quality hands, and the model's ability to condition image generation on hand parameters contributes to its success. The model's architecture and training process are detailed in the paper, along with the results of user studies that validate its effectiveness.